Abstract: The pharmaceutical industry faces significant challenges in developing novel medications, including high failure rates, substantial financial investments, and prolonged development timelines. Consequently, drug repurposing, identifying new therapeutic uses for existing drugs, has gained considerable attention. This strategy offers several advantages, such as utilizing compounds with established safety profiles, reducing development costs, and accelerating the time-to-market. A variety of computational and experimental methods have been proposed to identify suitable drug repurposing candidates. These approaches often leverage publicly accessible drug databases and integrate data from fundamental and translational research, clinical trial results, anecdotal reports of off-label drug use, and other available human data sources. By employing artificial intelligence algorithms and other bioinformatics tools, researchers can systematically analyse drug–protein interactions. These analyses can be further enhanced by incorporating genetic data, clinical observations, molecular structural information (e.g., through molecular docking), biological pathways, disease signatures, drug targets, phenotypic outcomes, binding assays, and AI-driven predictions. This review provides a comprehensive overview of the strategies, approaches, and methodologies applicable to drug repurposing, also known as drug repositioning. Additionally, it presents a compilation of successfully repurposed drugs along with their corresponding therapeutic indications.
Mandawar et al. (Tue,) studied this question.